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Record W2106457515 · doi:10.1080/13561820802303664

Measuring the quality of transdisciplinary teams

2008· article· en· W2106457515 on OpenAlexaff
Beata Batorowicz, Tracy A. Shepherd

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsThames Valley Children's CentreWestern University
Fundersnot available
KeywordsCronbach's alphaVarimax rotationTeamworkReliability (semiconductor)Construct validityIntraclass correlationPsychologyScale (ratio)Quality (philosophy)Applied psychologyHealth careTest (biology)PsychometricsNursingClinical psychologyMedicine

Abstract

fetched live from OpenAlex

The Team Decision Making Questionnaire (TDMQ) demonstrated internal consistency, stability over time, and construct validity. Internal consistencies were excellent and Cronbach's Alphas (N = 102) for the 4 components ranged from 0.83 to 0.91. The internal consistency for the total instrument was 0.96. Test re-test reliability (N = 22) measured with Intraclass Correlation Coefficient was good. Transdisciplinary teamwork is widely practiced in health care. However, specific measures to evaluate transdisciplinary team decision-making are not described in the literature. The purpose of this study was to develop and psychometrically test a scale to measure the quality of transdisciplinary teamwork. A multi-method approach using focus groups, field testing, and quantitative instrument development procedures was used to develop and evaluate TDMQ. Principal component analysis (PCA) with a varimax rotation (N = 102) revealed a four-component solution resulting in a 19-item measure consisting of 4 subscales including Decision Making, Team Support, Learning, and Developing Quality Services. This study's findings support the use of the TDMQ for measuring the benefits of transdisciplinary teamwork. The four subscales of the measure provide insight into the nature of such benefits. To validate the TDMQ research is required with a greater number of health care professionals and in other clinical fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.479
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2008
Admission routes1
Has abstractyes

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